Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

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Context Aware Computing for The Internet of Things: A Survey

Context-aware systems offer entirely new opportunities for application developers and for end users by gathering context data and adapting systems behaviour accordingly. Especially in combination with mobile devices, these mechanisms are of high value and are used to increase usability tremendously. In this paper, we present common architecture principles of context-aware systems and derive a layered conceptual design framework to explain the different elements common to most context-aware architectures. Based on these design principles, we introduce various existing context-aware systems focusing on context-aware middleware and frameworks, which ease the development of context-aware applications. We discuss various approaches and analyse important aspects in context-aware computing on the basis of the presented systems.

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A survey on context-aware systems

In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

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Situations and Attitudes.

Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

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Mobile Edge Computing: A Survey

Context-awareness is one of the drivers of the ubiquitous computing paradigm, whereas a well designed model is a key accessor to the context in any context-aware system This paper provides a survey of the the most relevant current approaches to modeling context for ubiquitous computing Numerous approaches are reviewed, classified relative to their core elements and evaluated with respect to their appropriateness for ubiquitous computing

A Context Modeling Survey

This paper describes a context modelling approach using ontologies as a formal fundament. We introduce our Aspect-Scale-Context (ASC) model and show how it is related to some other models. A Context Ontology Language (CoOL) is derived from the model, which may be used to enable context-awareness and contextual interoperability during service discovery and execution in a proposed distributed system architecture. A core component of this architecture is a reasoner which infers conclusions about the context based on an ontology built with CoOL.

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CoOL: A Context Ontology Language to Enable Contextual Interoperability

The prominence of systems for automatic person identification has risen increasingly during the past years. One biometric technique for unintrusive identification is gait recognition which offers the possibility to recognize and identify movement patterns of persons from some distance away. In former work, gait recognition is mainly achieved with camera systems. In this paper, we present an approach for gait recognition based on Microsoft Kinect, a peripheral for the gaming console XBOX 360, with an integrated depth sensor alowing for skeleton detection and tracking in realtime. We evaluate a number of body features together with steplength and speed, their relevance for person identification, and present the results of an empirical evaluation of our system, where we were able to accomplish a success rate of more than 90% with nine test persons.

Gait Recognition with Kinect

Location-based services turned out not to be the "next big thing" following the success stories of GSM and SMS. The reasons for this are manifold and range from inaccurate cellular positioning technologies to a lack of competition in this field, both being closely related to the fact that positioning is controlled by a network-centric approach where the mobile network operator has the unique selling point for making position data available to third parties. However, things change: small, low-cost GPS receivers, either attachable to mobile devices or even integrated, are enjoying great popularity since their recent inception and are expected to become a standard feature of cell phones in the near future. In conjunction with mobile packet data services, they provide a basis for device-centric LBS platforms, where position data can be obtained directly from the mobile device. In this article the device-centric LBS middleware TraX, which focuses particularly on position management, advanced functions for interrelating the position data of several targets, and privacy protection, is presented. Due to its generic and open design, TraX can be reused for a broad range of different LBSs and thus fosters service diversity and multiprovider environments, both of which are essential for making the next generation of LBSs a success

TraX: a device-centric middleware framework for location-based services

This paper introduces a comprehensive framework that allows mobile users to access a variety of services provided by their current environment (e.g. print services). Novel to our approach is that selection and execution of services takes into account the user’s current context. Instead of being harassed by useless activities as service browsing or configuration issues, environmental services get seamlessly aligned to the user’s present task. Thus, the challenge is to develop a new service framework that fulfils these demands.

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Claudia Linnhoff-Popien

Papers

A Context Modeling Survey

CoOL: A Context Ontology Language to Enable Contextual Interoperability

Gait Recognition with Kinect

TraX: a device-centric middleware framework for location-based services

CAPEUS: An Architecture for Context-Aware Selection and Execution of Services